Customer-Churn-Prediction AmirhosseinHonardoust · COMPLETED
Customer churn prediction with Python using synthetic datasets. Includes data generation, feature engineering, and training with Logistic Regression, Random Forest, and Gradient Boosting. Improved pipeline applies hyperparameter tuning and threshold optimization to boost recall. Outputs metrics, reports, and charts.
github.com/AmirhosseinHonardoust/Customer-Churn-Prediction · ★ 27 · Forks 1 · Size 475 KB
SUMMARY
Technologies 9
Scored 9
Observed 9
Practices 6
Evidence 11
Skips 0
COVERAGE
Analyzed 21 files · 22 commits · 0 API calls
TECHNOLOGIES & DEPTH
TOML LANGUAGE Depth 70
1 files · PRODUCTION
Markdown LANGUAGE Depth 70
2 files · PRODUCTION
YAML LANGUAGE Depth 70
1 files · PRODUCTION
Python LANGUAGE Depth 70
13 files · PRODUCTION
pip BUILD_TOOL Depth 80
1 files · CONFIGURATION
Poetry BUILD_TOOL Depth 80
1 files · CONFIGURATION
NumPy LIBRARY Depth 81
8 files · PRODUCTION, TEST
pandas LIBRARY Depth 81
9 files · PRODUCTION, TEST
pytest TESTING Depth 41
2 files · TEST
PRACTICES
documentation · observedautomated_tests · observedcontinuous_integration · observedcontainerization · absentlinting · observedformatting · absent
ACTIVITY & OWNERSHIP
First commit 2025-09-09
Last commit 2026-07-07
Active months 2
Commits 22